Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system
The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been ch...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322300417X |
| _version_ | 1797389484983058432 |
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| author | Mahnoor Ahmed Hakan Tanyas Raphaël Huser Ashok Dahal Giacomo Titti Lisa Borgatti Mirko Francioni Luigi Lombardo |
| author_facet | Mahnoor Ahmed Hakan Tanyas Raphaël Huser Ashok Dahal Giacomo Titti Lisa Borgatti Mirko Francioni Luigi Lombardo |
| author_sort | Mahnoor Ahmed |
| collection | DOAJ |
| description | The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions.This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine. |
| first_indexed | 2024-03-08T22:57:41Z |
| format | Article |
| id | doaj.art-926836b2260749f9a537857947328a64 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-08T22:57:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-926836b2260749f9a537857947328a642023-12-16T06:06:37ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-12-01125103593Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting systemMahnoor Ahmed0Hakan Tanyas1Raphaël Huser2Ashok Dahal3Giacomo Titti4Lisa Borgatti5Mirko Francioni6Luigi Lombardo7Department of Pure and Applied Sciences, University of Urbino ‘Carlo Bo’, Campus Scientifico Enrico Mattei, Via Cà le Suore, 2/4, 61029 Urbino, Italy; Corresponding author.University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede, AE 7500, NetherlandsStatistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi ArabiaUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede, AE 7500, NetherlandsDepartment of Civil Chemical Environmental and Materials Engineering, Alma Mater Studiorum University of Bologna, Bologna, ItalyDepartment of Civil Chemical Environmental and Materials Engineering, Alma Mater Studiorum University of Bologna, Bologna, ItalyDepartment of Pure and Applied Sciences, University of Urbino ‘Carlo Bo’, Campus Scientifico Enrico Mattei, Via Cà le Suore, 2/4, 61029 Urbino, ItalyUniversity of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede, AE 7500, NetherlandsThe initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions.This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.http://www.sciencedirect.com/science/article/pii/S156984322300417XDynamic susceptibilityLandslide predictionEarly warning systemGeneralized additive models |
| spellingShingle | Mahnoor Ahmed Hakan Tanyas Raphaël Huser Ashok Dahal Giacomo Titti Lisa Borgatti Mirko Francioni Luigi Lombardo Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system International Journal of Applied Earth Observations and Geoinformation Dynamic susceptibility Landslide prediction Early warning system Generalized additive models |
| title | Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system |
| title_full | Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system |
| title_fullStr | Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system |
| title_full_unstemmed | Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system |
| title_short | Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system |
| title_sort | dynamic rainfall induced landslide susceptibility a step towards a unified forecasting system |
| topic | Dynamic susceptibility Landslide prediction Early warning system Generalized additive models |
| url | http://www.sciencedirect.com/science/article/pii/S156984322300417X |
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